{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"GenBio-PathFM: A State-of-the-Art Foundation Model for Histopathology","authors":"Kapse, S.; Aygu\u0308n, M.; Cole, E.; Lundberg, E.; Song, L.; Xing, E. P.","author_corresponding":"Elijah Cole","author_corresponding_institution":"GenBio AI","doi":"10.64898\/2026.03.17.712534","date":"2026-03-20","version":"1","type":"new results","license":"cc_by_nc_nd","category":"bioinformatics","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2026\/03\/20\/2026.03.17.712534.source.xml","abstract":"Recent advancements in histopathology foundation models (FMs) have largely been driven by scaling the training data, often utilizing massive proprietary datasets. However, the long-tailed distribution of morphological features in whole-slide images (WSIs) makes simple scaling inefficient, as common morphologies dominate the learning signal. We introduce GenBio-PathFM, a 1.1B-parameter FM that achieves state-of-the-art performance on public benchmarks while using a fraction of the training data required by current leading models. The efficiency of GenBio-PathFM is underpinned by two primary innovations: an automated data curation pipeline that prioritizes morphological diversity and a novel dual-stage learning strategy which we term JEDI (JEPA + DINO). Across the THUNDER, HEST, and PathoROB benchmarks, GenBio-PathFM demonstrates state-of-the-art accuracy and robustness. GenBio-PathFM is the strongest open-weight model to date and the only state-of-the-art model trained exclusively on public data.","published":"NA","server":"bioRxiv"}]}